class RedisTSDBTest(TestCase):
def setUp(self):
self.db = RedisTSDB(
rollups=(
# time in seconds, samples to keep
(10, 30), # 5 minutes at 10 seconds
(ONE_MINUTE, 120), # 2 hours at 1 minute
(ONE_HOUR, 24), # 1 days at 1 hour
(ONE_DAY, 30), # 30 days at 1 day
),
vnodes=64,
enable_frequency_sketches=True,
hosts={
i - 6: {'db': i} for i in xrange(6, 9)
},
)
def tearDown(self):
with self.db.cluster.all() as client:
client.flushdb()
def test_make_counter_key(self):
result = self.db.make_counter_key(TSDBModel.project, 1368889980, 1)
assert result == 'ts:1:1368889980:1'
result = self.db.make_counter_key(TSDBModel.project, 1368889980, 'foo')
assert result == 'ts:1:1368889980:33'
def test_get_model_key(self):
result = self.db.get_model_key(1)
assert result == 1
result = self.db.get_model_key('foo')
assert result == 'bf4e529197e56a48ae2737505b9736e4'
def test_simple(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.incr(TSDBModel.project, 1, dts[0])
self.db.incr(TSDBModel.project, 1, dts[1], count=3)
self.db.incr(TSDBModel.project, 1, dts[2])
self.db.incr_multi([
(TSDBModel.project, 1),
(TSDBModel.project, 2),
], dts[3], count=4)
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 4),
],
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 4),
],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 9,
2: 4,
}
self.db.merge(TSDBModel.project, 1, [2], now)
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 8),
],
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 13,
2: 0,
}
self.db.delete([TSDBModel.project], [1, 2], dts[0], dts[-1])
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 0,
2: 0,
}
def test_count_distinct(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
model = TSDBModel.users_affected_by_group
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.record(
model,
1,
('foo', 'bar'),
dts[0],
)
self.db.record(
model,
1,
('baz',),
dts[1],
)
self.db.record_multi((
(
model,
1,
('foo', 'bar', 'baz'),
),
(
model,
2,
('bar',),
),
), dts[2])
self.db.record(
model,
2,
('foo',),
dts[3],
)
assert self.db.get_distinct_counts_series(model, [1], dts[0], dts[-1], rollup=3600) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 0),
],
}
assert self.db.get_distinct_counts_series(model, [2], dts[0], dts[-1], rollup=3600) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 1),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1], rollup=3600)
assert results == {
1: 3,
2: 2,
}
assert self.db.get_distinct_counts_union(model, [], dts[0], dts[-1], rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1, 2], dts[0], dts[-1], rollup=3600) == 3
self.db.merge_distinct_counts(model, 1, [2], dts[0])
assert self.db.get_distinct_counts_series(model, [1], dts[0], dts[-1], rollup=3600) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 1),
],
}
assert self.db.get_distinct_counts_series(model, [2], dts[0], dts[-1], rollup=3600) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1], rollup=3600)
assert results == {
1: 3,
2: 0,
}
assert self.db.get_distinct_counts_union(model, [], dts[0], dts[-1], rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1], dts[0], dts[-1], rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [1, 2], dts[0], dts[-1], rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [2], dts[0], dts[-1], rollup=3600) == 0
self.db.delete_distinct_counts([model], [1, 2], dts[0], dts[-1])
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1])
assert results == {
1: 0,
2: 0,
}
def test_frequency_tables(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC)
model = TSDBModel.frequent_projects_by_organization
rollup = 3600
self.db.record_frequency_multi(
(
(model, {
'organization:1': {
"project:1": 1,
"project:2": 2,
"project:3": 3,
},
}),
),
now
)
self.db.record_frequency_multi(
(
(model, {
'organization:1': {
"project:1": 1,
"project:2": 2,
"project:3": 3,
"project:4": 4,
},
"organization:2": {
"project:5": 1.5,
},
}),
),
now - timedelta(hours=1),
)
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0),
('project:2', 2.0),
('project:1', 1.0),
],
'organization:2': [],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
limit=1,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0),
],
'organization:2': [],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0 + 3.0),
('project:2', 2.0 + 2.0),
('project:4', 4.0),
('project:1', 1.0 + 1.0),
],
'organization:2': [
('project:5', 1.5),
],
}
timestamp = int(to_timestamp(now) // rollup) * rollup
assert self.db.get_most_frequent_series(
model,
(
'organization:1',
'organization:2',
'organization:3',
),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
(timestamp - rollup, {
'project:1': 1.0,
'project:2': 2.0,
'project:3': 3.0,
'project:4': 4.0,
}),
(timestamp, {
'project:1': 1.0,
'project:2': 2.0,
'project:3': 3.0,
}),
],
'organization:2': [
(timestamp - rollup, {
'project:5': 1.5,
}),
(timestamp, {}),
],
'organization:3': [
(timestamp - rollup, {}),
(timestamp, {}),
],
}
assert self.db.get_frequency_series(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4"),
'organization:2': ("project:5",),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
(timestamp - rollup, {
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 4.0,
}),
(timestamp, {
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 0.0,
}),
],
'organization:2': [
(timestamp - rollup, {
"project:5": 1.5,
}),
(timestamp, {
"project:5": 0.0,
}),
],
}
assert self.db.get_frequency_totals(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4", "project:5"),
'organization:2': ("project:1",),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': {
"project:1": 1.0 + 1.0,
"project:2": 2.0 + 2.0,
"project:3": 3.0 + 3.0,
"project:4": 4.0,
"project:5": 0.0,
},
'organization:2': {
"project:1": 0.0,
},
}
self.db.delete_frequencies(
[model],
['organization:1', 'organization:2'],
now - timedelta(hours=1),
now,
)
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
) == {
'organization:1': [],
'organization:2': [],
}
def test_frequency_table_import_export_no_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 10]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1, 'foo',
2, 'bar',
3, 'baz',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1, 'alpha',
2, 'beta',
3, 'gamma',
4, 'delta',
5, 'epsilon',
6, 'zeta',
7, 'eta',
8, 'theta',
9, 'iota',
],
client=client,
)
assert client.exists('1:i')
assert not client.exists('1:e')
assert client.exists('2:i')
assert not client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
def test_frequency_table_import_export_both_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1, 'foo',
2, 'bar',
3, 'baz',
4, 'wilco',
5, 'tango',
6, 'foxtrot',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1, 'alpha',
2, 'beta',
3, 'gamma',
4, 'delta',
5, 'epsilon',
6, 'zeta',
7, 'eta',
8, 'theta',
9, 'iota',
],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert client.exists('2:i')
assert client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['theta', '8'],
['eta', '7'],
['zeta', '6'],
['foxtrot', '6'],
]
def test_frequency_table_import_export_source_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
5, 'foo',
7, 'bar',
9, 'baz',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1, 'alpha',
2, 'beta',
3, 'gamma',
4, 'delta',
5, 'epsilon',
6, 'zeta',
7, 'eta',
8, 'theta',
9, 'iota',
],
client=client,
)
assert client.exists('1:i')
assert not client.exists('1:e')
assert client.exists('2:i')
assert client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['baz', '9'],
['theta', '8'],
['eta', '7'],
['bar', '7'],
]
def test_frequency_table_import_export_destination_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1, 'alpha',
2, 'beta',
3, 'gamma',
4, 'delta',
5, 'epsilon',
6, 'zeta',
7, 'eta',
8, 'theta',
9, 'iota',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
5, 'foo',
7, 'bar',
9, 'baz',
],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert client.exists('2:i')
assert not client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['baz', '9'],
['theta', '8'],
['eta', '7'],
['bar', '7'],
]
class RedisTSDBTest(TestCase):
def setUp(self):
self.db = RedisTSDB(
rollups=(
# time in seconds, samples to keep
(10, 30), # 5 minutes at 10 seconds
(ONE_MINUTE, 120), # 2 hours at 1 minute
(ONE_HOUR, 24), # 1 days at 1 hour
(ONE_DAY, 30), # 30 days at 1 day
),
vnodes=64,
enable_frequency_sketches=True,
hosts={i - 6: {
"db": i
}
for i in range(6, 9)},
)
def tearDown(self):
with self.db.cluster.all() as client:
client.flushdb()
def test_make_counter_key(self):
result = self.db.make_counter_key(TSDBModel.project, 1,
to_datetime(1368889980), 1, None)
assert result == ("ts:1:1368889980:1", 1)
result = self.db.make_counter_key(TSDBModel.project, 1,
to_datetime(1368889980), "foo", None)
assert result == ("ts:1:1368889980:46", self.db.get_model_key("foo"))
result = self.db.make_counter_key(TSDBModel.project, 1,
to_datetime(1368889980), 1, 1)
assert result == ("ts:1:1368889980:1", "1?e=1")
result = self.db.make_counter_key(TSDBModel.project, 1,
to_datetime(1368889980), "foo", 1)
assert result == ("ts:1:1368889980:46",
self.db.get_model_key("foo") + "?e=1")
def test_get_model_key(self):
result = self.db.get_model_key(1)
assert result == 1
result = self.db.get_model_key("foo")
assert result == "bf4e529197e56a48ae2737505b9736e4"
result = self.db.get_model_key(u"我爱啤酒")
assert result == "26f980fbe1e8a9d3a0123d2049f95f28"
def test_simple(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.incr(TSDBModel.project, 1, dts[0])
self.db.incr(TSDBModel.project, 1, dts[1], count=2)
self.db.incr(TSDBModel.project, 1, dts[1], environment_id=1)
self.db.incr(TSDBModel.project, 1, dts[2])
self.db.incr_multi([(TSDBModel.project, 1), (TSDBModel.project, 2)],
dts[3],
count=3,
environment_id=1)
self.db.incr_multi([(TSDBModel.project, 1), (TSDBModel.project, 2)],
dts[3],
count=1,
environment_id=2)
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 4),
]
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 4),
]
}
results = self.db.get_range(TSDBModel.project, [1, 2],
dts[0],
dts[-1],
environment_ids=[1])
assert results == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 3),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 3),
],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {1: 9, 2: 4}
results = self.db.get_sums(TSDBModel.project, [1, 2],
dts[0],
dts[-1],
environment_id=1)
assert results == {1: 4, 2: 3}
results = self.db.get_sums(TSDBModel.project, [1, 2],
dts[0],
dts[-1],
environment_id=0)
assert results == {1: 0, 2: 0}
self.db.merge(TSDBModel.project,
1, [2],
now,
environment_ids=[0, 1, 2])
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 8),
]
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
]
}
results = self.db.get_range(TSDBModel.project, [1, 2],
dts[0],
dts[-1],
environment_ids=[1])
assert results == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 6),
],
2: [(timestamp(dts[i]), 0) for i in range(0, 4)],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {1: 13, 2: 0}
self.db.delete([TSDBModel.project], [1, 2],
dts[0],
dts[-1],
environment_ids=[0, 1, 2])
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {1: 0, 2: 0}
results = self.db.get_sums(TSDBModel.project, [1, 2],
dts[0],
dts[-1],
environment_id=1)
assert results == {1: 0, 2: 0}
def test_count_distinct(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
model = TSDBModel.users_affected_by_group
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.record(model, 1, ("foo", "bar"), dts[0])
self.db.record(model, 1, ("baz", ), dts[1], environment_id=1)
self.db.record_multi(
((model, 1, ("foo", "bar")), (model, 2, ("bar", ))), dts[2])
self.db.record(model, 1, ("baz", ), dts[2], environment_id=1)
self.db.record(model, 2, ("foo", ), dts[3])
assert self.db.get_distinct_counts_series(
model, [1], dts[0], dts[-1], rollup=3600) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 0),
]
}
assert self.db.get_distinct_counts_series(
model, [2], dts[0], dts[-1], rollup=3600) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 1),
]
}
assert self.db.get_distinct_counts_series(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1) == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 0),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2],
dts[0],
dts[-1],
rollup=3600)
assert results == {1: 3, 2: 2}
results = self.db.get_distinct_counts_totals(model, [1, 2],
dts[0],
dts[-1],
rollup=3600,
environment_id=1)
assert results == {1: 1, 2: 0}
results = self.db.get_distinct_counts_totals(model, [1, 2],
dts[0],
dts[-1],
rollup=3600,
environment_id=0)
assert results == {1: 0, 2: 0}
assert self.db.get_distinct_counts_union(model, [],
dts[0],
dts[-1],
rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1, 2],
dts[0],
dts[-1],
rollup=3600) == 3
assert (self.db.get_distinct_counts_union(model, [1, 2],
dts[0],
dts[-1],
rollup=3600,
environment_id=1) == 1)
assert (self.db.get_distinct_counts_union(model, [1, 2],
dts[0],
dts[-1],
rollup=3600,
environment_id=0) == 0)
self.db.merge_distinct_counts(model,
1, [2],
dts[0],
environment_ids=[0, 1])
assert self.db.get_distinct_counts_series(
model, [1], dts[0], dts[-1], rollup=3600) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 1),
]
}
assert self.db.get_distinct_counts_series(
model, [2], dts[0], dts[-1], rollup=3600) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
]
}
assert self.db.get_distinct_counts_series(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1) == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 0),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2],
dts[0],
dts[-1],
rollup=3600)
assert results == {1: 3, 2: 0}
assert self.db.get_distinct_counts_union(model, [],
dts[0],
dts[-1],
rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1],
dts[0],
dts[-1],
rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [1, 2],
dts[0],
dts[-1],
rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [2],
dts[0],
dts[-1],
rollup=3600) == 0
self.db.delete_distinct_counts([model], [1, 2],
dts[0],
dts[-1],
environment_ids=[0, 1])
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0],
dts[-1])
assert results == {1: 0, 2: 0}
results = self.db.get_distinct_counts_totals(model, [1, 2],
dts[0],
dts[-1],
environment_id=1)
assert results == {1: 0, 2: 0}
def test_frequency_tables(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC)
model = TSDBModel.frequent_issues_by_project
# None of the registered frequency tables actually support
# environments, so we have to pretend like one actually does
self.db.models_with_environment_support = self.db.models_with_environment_support | set(
[model])
rollup = 3600
self.db.record_frequency_multi(((model, {
"organization:1": {
"project:1": 1,
"project:2": 2,
"project:3": 3
}
}), ), now)
self.db.record_frequency_multi(
((
model,
{
"organization:1": {
"project:1": 1,
"project:2": 1,
"project:3": 1,
"project:4": 1,
},
"organization:2": {
"project:5": 1
},
},
), ),
now - timedelta(hours=1),
)
self.db.record_frequency_multi(
((
model,
{
"organization:1": {
"project:2": 1,
"project:3": 2,
"project:4": 3
},
"organization:2": {
"project:5": 0.5
},
},
), ),
now - timedelta(hours=1),
environment_id=1,
)
assert self.db.get_most_frequent(model,
("organization:1", "organization:2"),
now,
rollup=rollup) == {
"organization:1":
[("project:3", 3.0),
("project:2", 2.0),
("project:1", 1.0)],
"organization:2": [],
}
assert self.db.get_most_frequent(
model,
("organization:1", "organization:2"),
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
"organization:1": [("project:4", 3.0), ("project:3", 2.0),
("project:2", 1.0)],
"organization:2": [("project:5", 0.5)],
}
assert self.db.get_most_frequent(model,
("organization:1", "organization:2"),
now,
limit=1,
rollup=rollup) == {
"organization:1":
[("project:3", 3.0)],
"organization:2": []
}
assert self.db.get_most_frequent(
model,
("organization:1", "organization:2"),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
"organization:1": [
("project:3", 3.0 + 3.0),
("project:2", 2.0 + 2.0),
("project:4", 4.0),
("project:1", 1.0 + 1.0),
],
"organization:2": [("project:5", 1.5)],
}
assert self.db.get_most_frequent(
model,
("organization:1", "organization:2"),
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=0,
) == {
"organization:1": [],
"organization:2": []
}
timestamp = int(to_timestamp(now) // rollup) * rollup
assert self.db.get_most_frequent_series(
model,
("organization:1", "organization:2", "organization:3"),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
"organization:1": [
(
timestamp - rollup,
{
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 4.0
},
),
(timestamp, {
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0
}),
],
"organization:2": [(timestamp - rollup, {
"project:5": 1.5
}), (timestamp, {})],
"organization:3": [(timestamp - rollup, {}), (timestamp, {})],
}
assert self.db.get_frequency_series(
model,
{
"organization:1":
("project:1", "project:2", "project:3", "project:4"),
"organization:2": ("project:5", ),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
"organization:1": [
(
timestamp - rollup,
{
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 4.0
},
),
(
timestamp,
{
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 0.0
},
),
],
"organization:2": [
(timestamp - rollup, {
"project:5": 1.5
}),
(timestamp, {
"project:5": 0.0
}),
],
}
assert self.db.get_frequency_series(
model,
{
"organization:1":
("project:1", "project:2", "project:3", "project:4"),
"organization:2": ("project:5", ),
},
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
"organization:1": [
(
timestamp - rollup,
{
"project:1": 0.0,
"project:2": 1.0,
"project:3": 2.0,
"project:4": 3.0
},
),
(
timestamp,
{
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0
},
),
],
"organization:2": [
(timestamp - rollup, {
"project:5": 0.5
}),
(timestamp, {
"project:5": 0.0
}),
],
}
assert self.db.get_frequency_totals(
model,
{
"organization:1": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
"organization:2": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
"organization:1": {
"project:1": 1.0 + 1.0,
"project:2": 2.0 + 2.0,
"project:3": 3.0 + 3.0,
"project:4": 4.0,
"project:5": 0.0,
},
"organization:2": {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 1.5,
},
}
self.db.merge_frequencies(model,
"organization:1", ["organization:2"],
now,
environment_ids=[0, 1])
assert self.db.get_frequency_totals(
model,
{
"organization:1": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
"organization:2": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
"organization:1": {
"project:1": 1.0 + 1.0,
"project:2": 2.0 + 2.0,
"project:3": 3.0 + 3.0,
"project:4": 4.0,
"project:5": 1.5,
},
"organization:2": {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 0.0,
},
}
assert self.db.get_frequency_totals(
model,
{
"organization:1": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
"organization:2": ("project:1", "project:2", "project:3",
"project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
"organization:1": {
"project:1": 0.0,
"project:2": 1.0,
"project:3": 2.0,
"project:4": 3.0,
"project:5": 0.5,
},
"organization:2": {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 0.0,
},
}
self.db.delete_frequencies(
[model],
["organization:1", "organization:2"],
now - timedelta(hours=1),
now,
environment_ids=[0, 1],
)
assert self.db.get_most_frequent(model,
("organization:1", "organization:2"),
now) == {
"organization:1": [],
"organization:2": [],
}
assert self.db.get_most_frequent(model,
("organization:1", "organization:2"),
now,
environment_id=1) == {
"organization:1": [],
"organization:2": []
}
def test_frequency_table_import_export_no_estimators(self):
client = self.db.cluster.get_local_client_for_key("key")
parameters = [64, 5, 10]
CountMinScript(["1:i", "1:e"],
["INCR"] + parameters + [1, "foo", 2, "bar", 3, "baz"],
client=client)
CountMinScript(
["2:i", "2:e"],
["INCR"] + parameters + [
1,
"alpha",
2,
"beta",
3,
"gamma",
4,
"delta",
5,
"epsilon",
6,
"zeta",
7,
"eta",
8,
"theta",
9,
"iota",
],
client=client,
)
assert client.exists("1:i")
assert not client.exists("1:e")
assert client.exists("2:i")
assert not client.exists("2:e")
exports = CountMinScript(["2:i", "2:e"], ["EXPORT"] + parameters,
client=client)
assert len(exports) == 1
CountMinScript(["1:i", "1:e"], ["IMPORT"] + parameters + [exports[0]],
client=client)
assert client.exists("1:i")
assert client.exists("1:e")
def test_frequency_table_import_export_both_estimators(self):
client = self.db.cluster.get_local_client_for_key("key")
parameters = [64, 5, 5]
CountMinScript(
["1:i", "1:e"],
["INCR"] + parameters + [
1, "foo", 2, "bar", 3, "baz", 4, "wilco", 5, "tango", 6,
"foxtrot"
],
client=client,
)
CountMinScript(
["2:i", "2:e"],
["INCR"] + parameters + [
1,
"alpha",
2,
"beta",
3,
"gamma",
4,
"delta",
5,
"epsilon",
6,
"zeta",
7,
"eta",
8,
"theta",
9,
"iota",
],
client=client,
)
assert client.exists("1:i")
assert client.exists("1:e")
assert client.exists("2:i")
assert client.exists("2:e")
exports = CountMinScript(["2:i", "2:e"], ["EXPORT"] + parameters,
client=client)
assert len(exports) == 1
CountMinScript(["1:i", "1:e"], ["IMPORT"] + parameters + [exports[0]],
client=client)
assert client.exists("1:i")
assert client.exists("1:e")
assert CountMinScript(["1:i", "1:e"], ["RANKED"] + parameters,
client=client) == [
[b"iota", b"9"],
[b"theta", b"8"],
[b"eta", b"7"],
[b"zeta", b"6"],
[b"foxtrot", b"6"],
]
def test_frequency_table_import_export_source_estimators(self):
client = self.db.cluster.get_local_client_for_key("key")
parameters = [64, 5, 5]
CountMinScript(["1:i", "1:e"],
["INCR"] + parameters + [5, "foo", 7, "bar", 9, "baz"],
client=client)
CountMinScript(
["2:i", "2:e"],
["INCR"] + parameters + [
1,
"alpha",
2,
"beta",
3,
"gamma",
4,
"delta",
5,
"epsilon",
6,
"zeta",
7,
"eta",
8,
"theta",
9,
"iota",
],
client=client,
)
assert client.exists("1:i")
assert not client.exists("1:e")
assert client.exists("2:i")
assert client.exists("2:e")
exports = CountMinScript(["2:i", "2:e"], ["EXPORT"] + parameters,
client=client)
assert len(exports) == 1
CountMinScript(["1:i", "1:e"], ["IMPORT"] + parameters + [exports[0]],
client=client)
assert client.exists("1:i")
assert client.exists("1:e")
assert CountMinScript(["1:i", "1:e"], ["RANKED"] + parameters,
client=client) == [
[b"iota", b"9"],
[b"baz", b"9"],
[b"theta", b"8"],
[b"eta", b"7"],
[b"bar", b"7"],
]
def test_frequency_table_import_export_destination_estimators(self):
client = self.db.cluster.get_local_client_for_key("key")
parameters = [64, 5, 5]
CountMinScript(
["1:i", "1:e"],
["INCR"] + parameters + [
1,
"alpha",
2,
"beta",
3,
"gamma",
4,
"delta",
5,
"epsilon",
6,
"zeta",
7,
"eta",
8,
"theta",
9,
"iota",
],
client=client,
)
CountMinScript(["2:i", "2:e"],
["INCR"] + parameters + [5, "foo", 7, "bar", 9, "baz"],
client=client)
assert client.exists("1:i")
assert client.exists("1:e")
assert client.exists("2:i")
assert not client.exists("2:e")
exports = CountMinScript(["2:i", "2:e"], ["EXPORT"] + parameters,
client=client)
assert len(exports) == 1
CountMinScript(["1:i", "1:e"], ["IMPORT"] + parameters + [exports[0]],
client=client)
assert client.exists("1:i")
assert client.exists("1:e")
assert CountMinScript(["1:i", "1:e"], ["RANKED"] + parameters,
client=client) == [
[b"iota", b"9"],
[b"baz", b"9"],
[b"theta", b"8"],
[b"eta", b"7"],
[b"bar", b"7"],
]
class RedisTSDBTest(TestCase):
def setUp(self):
self.db = RedisTSDB(
rollups=(
# time in seconds, samples to keep
(10, 30), # 5 minutes at 10 seconds
(ONE_MINUTE, 120), # 2 hours at 1 minute
(ONE_HOUR, 24), # 1 days at 1 hour
(ONE_DAY, 30), # 30 days at 1 day
),
vnodes=64,
enable_frequency_sketches=True,
hosts={i - 6: {
'db': i
} for i in range(6, 9)},
)
def tearDown(self):
with self.db.cluster.all() as client:
client.flushdb()
def test_make_counter_key(self):
result = self.db.make_counter_key(TSDBModel.project, 1, to_datetime(1368889980), 1, None)
assert result == ('ts:1:1368889980:1', 1)
result = self.db.make_counter_key(
TSDBModel.project, 1, to_datetime(1368889980), 'foo', None)
assert result == ('ts:1:1368889980:46', self.db.get_model_key('foo'))
result = self.db.make_counter_key(TSDBModel.project, 1, to_datetime(1368889980), 1, 1)
assert result == ('ts:1:1368889980:1', '1?e=1')
result = self.db.make_counter_key(TSDBModel.project, 1, to_datetime(1368889980), 'foo', 1)
assert result == ('ts:1:1368889980:46', self.db.get_model_key('foo') + '?e=1')
def test_get_model_key(self):
result = self.db.get_model_key(1)
assert result == 1
result = self.db.get_model_key('foo')
assert result == 'bf4e529197e56a48ae2737505b9736e4'
def test_simple(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.incr(TSDBModel.project, 1, dts[0])
self.db.incr(TSDBModel.project, 1, dts[1], count=2)
self.db.incr(TSDBModel.project, 1, dts[1], environment_id=1)
self.db.incr(TSDBModel.project, 1, dts[2])
self.db.incr_multi(
[
(TSDBModel.project, 1),
(TSDBModel.project, 2),
], dts[3], count=3, environment_id=1
)
self.db.incr_multi(
[
(TSDBModel.project, 1),
(TSDBModel.project, 2),
], dts[3], count=1, environment_id=2
)
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 4),
],
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 4),
],
}
results = self.db.get_range(TSDBModel.project, [1, 2], dts[0], dts[-1], environment_id=1)
assert results == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 3),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 3),
],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 9,
2: 4,
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1], environment_id=1)
assert results == {
1: 4,
2: 3,
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1], environment_id=0)
assert results == {
1: 0,
2: 0,
}
self.db.merge(TSDBModel.project, 1, [2], now, environment_ids=[0, 1, 2])
results = self.db.get_range(TSDBModel.project, [1], dts[0], dts[-1])
assert results == {
1: [
(timestamp(dts[0]), 1),
(timestamp(dts[1]), 3),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 8),
],
}
results = self.db.get_range(TSDBModel.project, [2], dts[0], dts[-1])
assert results == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_range(TSDBModel.project, [1, 2], dts[0], dts[-1], environment_id=1)
assert results == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 6),
],
2: [(timestamp(dts[i]), 0) for i in range(0, 4)],
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 13,
2: 0,
}
self.db.delete([TSDBModel.project], [1, 2], dts[0], dts[-1], environment_ids=[0, 1, 2])
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1])
assert results == {
1: 0,
2: 0,
}
results = self.db.get_sums(TSDBModel.project, [1, 2], dts[0], dts[-1], environment_id=1)
assert results == {
1: 0,
2: 0,
}
def test_count_distinct(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC) - timedelta(hours=4)
dts = [now + timedelta(hours=i) for i in range(4)]
model = TSDBModel.users_affected_by_group
def timestamp(d):
t = int(to_timestamp(d))
return t - (t % 3600)
self.db.record(
model,
1,
('foo', 'bar'),
dts[0],
)
self.db.record(
model,
1,
('baz', ),
dts[1],
environment_id=1,
)
self.db.record_multi(
((model, 1, ('foo', 'bar'), ), (model, 2, ('bar', ), ), ), dts[2]
)
self.db.record(
model,
1,
('baz', ),
dts[2],
environment_id=1,
)
self.db.record(
model,
2,
('foo', ),
dts[3],
)
assert self.db.get_distinct_counts_series(
model, [1], dts[0], dts[-1], rollup=3600
) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 0),
],
}
assert self.db.get_distinct_counts_series(
model, [2], dts[0], dts[-1], rollup=3600
) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 1),
],
}
assert self.db.get_distinct_counts_series(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1,
) == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 0),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1], rollup=3600)
assert results == {
1: 3,
2: 2,
}
results = self.db.get_distinct_counts_totals(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1)
assert results == {
1: 1,
2: 0,
}
results = self.db.get_distinct_counts_totals(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=0)
assert results == {
1: 0,
2: 0,
}
assert self.db.get_distinct_counts_union(model, [], dts[0], dts[-1], rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1, 2], dts[0], dts[-1], rollup=3600) == 3
assert self.db.get_distinct_counts_union(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1) == 1
assert self.db.get_distinct_counts_union(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=0) == 0
self.db.merge_distinct_counts(model, 1, [2], dts[0], environment_ids=[0, 1])
assert self.db.get_distinct_counts_series(
model, [1], dts[0], dts[-1], rollup=3600
) == {
1: [
(timestamp(dts[0]), 2),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 3),
(timestamp(dts[3]), 1),
],
}
assert self.db.get_distinct_counts_series(
model, [2], dts[0], dts[-1], rollup=3600
) == {
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
assert self.db.get_distinct_counts_series(
model, [1, 2], dts[0], dts[-1], rollup=3600, environment_id=1,
) == {
1: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 1),
(timestamp(dts[2]), 1),
(timestamp(dts[3]), 0),
],
2: [
(timestamp(dts[0]), 0),
(timestamp(dts[1]), 0),
(timestamp(dts[2]), 0),
(timestamp(dts[3]), 0),
],
}
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1], rollup=3600)
assert results == {
1: 3,
2: 0,
}
assert self.db.get_distinct_counts_union(model, [], dts[0], dts[-1], rollup=3600) == 0
assert self.db.get_distinct_counts_union(model, [1], dts[0], dts[-1], rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [1, 2], dts[0], dts[-1], rollup=3600) == 3
assert self.db.get_distinct_counts_union(model, [2], dts[0], dts[-1], rollup=3600) == 0
self.db.delete_distinct_counts([model], [1, 2], dts[0], dts[-1], environment_ids=[0, 1])
results = self.db.get_distinct_counts_totals(model, [1, 2], dts[0], dts[-1])
assert results == {
1: 0,
2: 0,
}
results = self.db.get_distinct_counts_totals(
model, [1, 2], dts[0], dts[-1], environment_id=1)
assert results == {
1: 0,
2: 0,
}
def test_frequency_tables(self):
now = datetime.utcnow().replace(tzinfo=pytz.UTC)
model = TSDBModel.frequent_projects_by_organization
# None of the registered frequency tables actually support
# environments, so we have to pretend like one actually does
self.db.models_with_environment_support = self.db.models_with_environment_support | set([
model])
rollup = 3600
self.db.record_frequency_multi(
((model, {
'organization:1': {
"project:1": 1,
"project:2": 2,
"project:3": 3,
},
}), ), now
)
self.db.record_frequency_multi(
(
(
model, {
'organization:1': {
"project:1": 1,
"project:2": 1,
"project:3": 1,
"project:4": 1,
},
"organization:2": {
"project:5": 1,
},
}
),
),
now - timedelta(hours=1),
)
self.db.record_frequency_multi(
(
(
model, {
'organization:1': {
"project:2": 1,
"project:3": 2,
"project:4": 3,
},
"organization:2": {
"project:5": 0.5,
},
}
),
),
now - timedelta(hours=1),
environment_id=1,
)
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0),
('project:2', 2.0),
('project:1', 1.0),
],
'organization:2': [],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
'organization:1': [
('project:4', 3.0),
('project:3', 2.0),
('project:2', 1.0),
],
'organization:2': [
('project:5', 0.5),
],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
limit=1,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0),
],
'organization:2': [],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
('project:3', 3.0 + 3.0),
('project:2', 2.0 + 2.0),
('project:4', 4.0),
('project:1', 1.0 + 1.0),
],
'organization:2': [
('project:5', 1.5),
],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=0,
) == {
'organization:1': [
],
'organization:2': [
],
}
timestamp = int(to_timestamp(now) // rollup) * rollup
assert self.db.get_most_frequent_series(
model,
('organization:1', 'organization:2', 'organization:3', ),
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
(
timestamp - rollup, {
'project:1': 1.0,
'project:2': 2.0,
'project:3': 3.0,
'project:4': 4.0,
}
),
(timestamp, {
'project:1': 1.0,
'project:2': 2.0,
'project:3': 3.0,
}),
],
'organization:2': [
(timestamp - rollup, {
'project:5': 1.5,
}),
(timestamp, {}),
],
'organization:3': [
(timestamp - rollup, {}),
(timestamp, {}),
],
}
assert self.db.get_frequency_series(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4"),
'organization:2': ("project:5", ),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': [
(
timestamp - rollup, {
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 4.0,
}
),
(
timestamp, {
"project:1": 1.0,
"project:2": 2.0,
"project:3": 3.0,
"project:4": 0.0,
}
),
],
'organization:2': [
(timestamp - rollup, {
"project:5": 1.5,
}),
(timestamp, {
"project:5": 0.0,
}),
],
}
assert self.db.get_frequency_series(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4"),
'organization:2': ("project:5", ),
},
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
'organization:1': [
(
timestamp - rollup, {
"project:1": 0.0,
"project:2": 1.0,
"project:3": 2.0,
"project:4": 3.0,
}
),
(
timestamp, {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
}
),
],
'organization:2': [
(timestamp - rollup, {
"project:5": 0.5,
}),
(timestamp, {
"project:5": 0.0,
}),
],
}
assert self.db.get_frequency_totals(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4", "project:5"),
'organization:2': ("project:1", "project:2", "project:3", "project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': {
"project:1": 1.0 + 1.0,
"project:2": 2.0 + 2.0,
"project:3": 3.0 + 3.0,
"project:4": 4.0,
"project:5": 0.0,
},
'organization:2': {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 1.5,
},
}
self.db.merge_frequencies(
model,
'organization:1',
['organization:2'],
now,
environment_ids=[0, 1],
)
assert self.db.get_frequency_totals(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4", "project:5"),
'organization:2': ("project:1", "project:2", "project:3", "project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
) == {
'organization:1': {
"project:1": 1.0 + 1.0,
"project:2": 2.0 + 2.0,
"project:3": 3.0 + 3.0,
"project:4": 4.0,
"project:5": 1.5,
},
'organization:2': {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 0.0,
},
}
assert self.db.get_frequency_totals(
model,
{
'organization:1': ("project:1", "project:2", "project:3", "project:4", "project:5"),
'organization:2': ("project:1", "project:2", "project:3", "project:4", "project:5"),
},
now - timedelta(hours=1),
now,
rollup=rollup,
environment_id=1,
) == {
'organization:1': {
"project:1": 0.0,
"project:2": 1.0,
"project:3": 2.0,
"project:4": 3.0,
"project:5": 0.5,
},
'organization:2': {
"project:1": 0.0,
"project:2": 0.0,
"project:3": 0.0,
"project:4": 0.0,
"project:5": 0.0,
},
}
self.db.delete_frequencies(
[model],
['organization:1', 'organization:2'],
now - timedelta(hours=1),
now,
environment_ids=[0, 1],
)
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
) == {
'organization:1': [],
'organization:2': [],
}
assert self.db.get_most_frequent(
model,
('organization:1', 'organization:2'),
now,
environment_id=1,
) == {
'organization:1': [],
'organization:2': [],
}
def test_frequency_table_import_export_no_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 10]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1,
'foo',
2,
'bar',
3,
'baz',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1,
'alpha',
2,
'beta',
3,
'gamma',
4,
'delta',
5,
'epsilon',
6,
'zeta',
7,
'eta',
8,
'theta',
9,
'iota',
],
client=client,
)
assert client.exists('1:i')
assert not client.exists('1:e')
assert client.exists('2:i')
assert not client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
def test_frequency_table_import_export_both_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1,
'foo',
2,
'bar',
3,
'baz',
4,
'wilco',
5,
'tango',
6,
'foxtrot',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1,
'alpha',
2,
'beta',
3,
'gamma',
4,
'delta',
5,
'epsilon',
6,
'zeta',
7,
'eta',
8,
'theta',
9,
'iota',
],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert client.exists('2:i')
assert client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['theta', '8'],
['eta', '7'],
['zeta', '6'],
['foxtrot', '6'],
]
def test_frequency_table_import_export_source_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
5,
'foo',
7,
'bar',
9,
'baz',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
1,
'alpha',
2,
'beta',
3,
'gamma',
4,
'delta',
5,
'epsilon',
6,
'zeta',
7,
'eta',
8,
'theta',
9,
'iota',
],
client=client,
)
assert client.exists('1:i')
assert not client.exists('1:e')
assert client.exists('2:i')
assert client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['baz', '9'],
['theta', '8'],
['eta', '7'],
['bar', '7'],
]
def test_frequency_table_import_export_destination_estimators(self):
client = self.db.cluster.get_local_client_for_key('key')
parameters = [64, 5, 5]
CountMinScript(
['1:i', '1:e'],
['INCR'] + parameters + [
1,
'alpha',
2,
'beta',
3,
'gamma',
4,
'delta',
5,
'epsilon',
6,
'zeta',
7,
'eta',
8,
'theta',
9,
'iota',
],
client=client,
)
CountMinScript(
['2:i', '2:e'],
['INCR'] + parameters + [
5,
'foo',
7,
'bar',
9,
'baz',
],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert client.exists('2:i')
assert not client.exists('2:e')
exports = CountMinScript(
['2:i', '2:e'],
['EXPORT'] + parameters,
client=client,
)
assert len(exports) == 1
CountMinScript(
['1:i', '1:e'],
['IMPORT'] + parameters + [exports[0]],
client=client,
)
assert client.exists('1:i')
assert client.exists('1:e')
assert CountMinScript(
['1:i', '1:e'],
['RANKED'] + parameters,
client=client,
) == [
['iota', '9'],
['baz', '9'],
['theta', '8'],
['eta', '7'],
['bar', '7'],
]